Graph Convolutional Network for Predicting Secondary Structure of RNA

Overview

Journal Res Sq

Date 2024 Mar 11

PMID 38464300

Authors

Palawat Busaranuvong

Aukkawut Ammartayakun

Dmitry Korkin

Roya Khosravi-Far

Affiliations

Soon will be listed here.

Abstract

The prediction of RNA secondary structures is essential for understanding its underlying principles and applications in diverse fields, including molecular diagnostics and RNA-based therapeutic strategies. However, the complexity of the search space presents a challenge. This work proposes a Graph Convolutional Network (GCNfold) for predicting the RNA secondary structure. GCNfold considers an RNA sequence as graph-structured data and predicts posterior base-pairing probabilities given the prior base-pairing probabilities, calculated using McCaskill's partition function. The performance of GCNfold surpasses that of the state-of-the-art folding algorithms, as we have incorporated minimum free energy information into the richly parameterized network, enhancing its robustness in predicting non-homologous RNA secondary structures. A Symmetric Argmax Post-processing algorithm ensures that GCNfold formulates valid structures. To validate our algorithm, we applied it to the SARS-CoV-2 E gene and determined the secondary structure of the E-gene across the subgenera.

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